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<title cf:type="text"><![CDATA[Editorial department of the Journal of National University of Defense Technology -->专题：通信系统的学习问题]]></title>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Stepwise routing algorithm in mobile ad hoc network under reinforcement learning framework]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202004001]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Mobile ad hoc network is a communication network formed by mobile nodes with non-infrastructure, which has highly dynamic characteristics. Conventional routing protocols cannot adapt to the frequent topology changes brought by node mobility, and the flooding routing also causes the network performance degradation due to the excessive routing overhead. A stepwise routing algorithm based on reinforcement learning was proposed for adaptive routing in mobile ad hoc networks. This algorithm aims at total round trip time minimization and uses the reinforcement learning algorithm to select the next hop. After selecting the set of nodes that meet the requirements of the target, it combines the confidence parameters to select the route. When the link becomes unreliable, packets are broadcasted to filtered neighbor nodes to improve the reliability and reduce the routing overhead. The main property indication of the proposed algorithm, such as throughput and routing overhead, were analyzed theoretically. The simulation results show that, compared with the reinforcement learning based smart robust routing, the proposed routing algorithm reduces the overhead and maintains a competitive throughput.]]></description>
<pubDate>2020/8/8 11:01:27</pubDate>
<category><![CDATA[专题：通信系统的学习问题]]></category>
<author><![CDATA[KUAI Zhenran, WANG Shaowei]]></author>
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<atom:name>KUAI Zhenran, WANG Shaowei</atom:name>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[IQ imbalance compensation algorithm with deep neural network in OFDM systems]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202004002]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[OFDM (orthogonal frequency division multiplexing) is an essential technique in the physical layer of wireless communications, and OFDM system requires rigid orthogonality between subcarriers. However, in practical systems, the imperfection of components like the oscillator and filter would introduce IQ(in-phase and quadrature-phase) imbalance into the system. The IQ imbalance would infect the orthogonality between subcarriers and decrease the system performance. The effect of IQ imbalance was discussed and an IQ imbalance compensation algorithm with the guidance of parallel DNN (deep neural network) was proposed. The deep neural network relies rarely on mathematic models, and the proposed algorithm utilizes this feature to recover the original signal from the received signal in the frequency domain to its original binary sequence of transmitted signal directly. Meanwhile, the prior knowledge that the interference comes from the image aliasing effect was utilized to initialize the model-driven neural network. Simulation results proves that the proposed algorithm can effectively compensate IQ imbalance distortion, and it outperforms traditional LS algorithm based on pilots in both amplitude and phase compensation and proves the superiority of deep learning solutions for issues in the physical layer.]]></description>
<pubDate>2020/8/8 11:01:28</pubDate>
<category><![CDATA[专题：通信系统的学习问题]]></category>
<author><![CDATA[LIU Siqi, WANG Tianyu, WANG Shaowei]]></author>
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<atom:name>LIU Siqi, WANG Tianyu, WANG Shaowei</atom:name>
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<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202004002]]></guid><cfi:id>5</cfi:id><cfi:read>true</cfi:read></item>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Online learning for primary user emulation attack strategy]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202004003]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[In cognitive radio system, the secondary users learn radio spectrum environment through spectrum sensing to get the spectrum holes that the primary users do not occupy. In practice, the existence of various malicious attacks can seriously affect the reliability of spectrum sensing of the secondary users. Only in-depth study of the malicious attack strategies can ensure the security of cognitive radio networks. Based on this, a spoofing jamming strategy in cognitive wireless network, called as primary user emulation attack strategy, was studied. The strategy deteriorates the spectrum sensing performance of the secondary users by transmitting the forged primary user signals over channels. More concretely, the attack strategy was modeled as an online learning problem, and a Thompson sampling based attack strategy was proposed to find an efficient tradeoff between the exploitation of high-performance channels and the exploration of uncertain channels. The simulation results show that compared with the existing attack strategy, the proposed attack strategy can better adapt to the non-stationary cognitive wireless network by optimizing the attack decision through online learning.]]></description>
<pubDate>2020/8/8 11:01:28</pubDate>
<category><![CDATA[专题：通信系统的学习问题]]></category>
<author><![CDATA[SHENG Xiang, WANG Shaowei]]></author>
<atom:author xmlns:atom="http://www.w3.org/2005/Atom">
<atom:name>SHENG Xiang, WANG Shaowei</atom:name>
</atom:author>
<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202004003]]></guid><cfi:id>4</cfi:id><cfi:read>true</cfi:read></item>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Wireless coverage prediction algorithm under the guidance of deep neural network]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202004004]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[In order to adjust the parameters of cell antennas dynamically according to the real-time coverage in the new generation mobile wireless network, it is necessary to predict the wireless coverage efficiently and accurately. The traditional solution method is to judge the antenna parameters by accurate field strength prediction in the target area. The method is accurate but wastes large amounts of computing resources, which cannot meet the actual needs of 5G and beyond 5G mobile networks to dynamically adjust the radio frequency parameters through real-time coverage prediction. Here the algorithm based on deep neural network was proposed to predict the coverage under given antenna parameters in order to replace the accurate field strength prediction of the target area. Numerical results show that the algorithm can keep the accuracy of the calculation while significantly reducing the computing resources, which provides basic reference data for 5G dynamic network planning.]]></description>
<pubDate>2020/8/8 11:01:28</pubDate>
<category><![CDATA[专题：通信系统的学习问题]]></category>
<author><![CDATA[SHEN Linzhi, WANG Shaowei]]></author>
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<atom:name>SHEN Linzhi, WANG Shaowei</atom:name>
</atom:author>
<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202004004]]></guid><cfi:id>3</cfi:id><cfi:read>true</cfi:read></item>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Online optimal selection of spectrum sensing order]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202004005]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Dynamic spectrum access is deemed as an effective solution to the radio spectrum scarcity and spectrum usage in efficiency problem, which allows secondary users to access the spectrum dynamically for data transmission when the licensed spectrum is idle. However, spectrum sensing is one of the key challenges for dynamic spectrum access. Since the secondary user was equipped with limited sensing capability, in order to obtain more spectrum access opportunities, the spectrum sensing order problem was investigated to find the frequency band with the highest probability of being idle as soon as possible. Considering that the probability of the spectrum being idle was not available for the secondary users and changes over time, an online learning framework in which the spectrum sensing order problem was formulated as a classical multi-armed bandit problem was proposed, and it was addressed by using an online learning method, referred to as satisficing discounted Thompson sampling. Simulation results indicate that compared with other algorithms, the proposed algorithm yields more spectrum opportunities and can track the changes of the probability of the spectrum being idle.]]></description>
<pubDate>2020/8/8 11:01:28</pubDate>
<category><![CDATA[专题：通信系统的学习问题]]></category>
<author><![CDATA[ZHOU Min, WANG Shaowei]]></author>
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<atom:name>ZHOU Min, WANG Shaowei</atom:name>
</atom:author>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Network traffic classification method based on deep forest]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202004006]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[With the rapid development of network applications, the Internet traffic classification has a profound impact on the research fields of network resource allocation, traffic scheduling and network security. The traditional flow analysis method based on machine learning has strict requirements for the feature selection and distribution of network flows, which makes it difficult to accurately and stably classify the complex and changeable flow data in practical application. In order to solve the adverse impact of the complexity of sample features on the traffic classification, a new classification method based on deep forest, which utilizes the cascade forest of decision trees and the multi-grained scanning mechanisms aiming to improve classification performance in the case of limited scale of samples and features, was proposed. The machine learning algorithms including support vector machine, random forest and deep forest were trained and tested by using Moore, which is a flow data set in public domain. The experiment results show that the classification accuracy using deep forest model reaches 96.36%, which outperforms the other machine learning models.]]></description>
<pubDate>2020/8/8 11:01:28</pubDate>
<category><![CDATA[专题：通信系统的学习问题]]></category>
<author><![CDATA[DAI Jin, WANG Tianyu, WANG Shaowei]]></author>
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<atom:name>DAI Jin, WANG Tianyu, WANG Shaowei</atom:name>
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